Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations10000
Missing cells4357
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory387.8 B

Variable types

Text4
Numeric8
Categorical3

Alerts

danceability has constant value "0.0" Constant
energy has constant value "0.0" Constant
artist_familiarity is highly overall correlated with artist_hotttnesss and 1 other fieldsHigh correlation
artist_hotttnesss is highly overall correlated with artist_familiarity and 1 other fieldsHigh correlation
song_hotttnesss is highly overall correlated with artist_familiarity and 1 other fieldsHigh correlation
song_hotttnesss has 4352 (43.5%) missing values Missing
track_id has unique values Unique
song_id has unique values Unique
year has 5320 (53.2%) zeros Zeros
key has 1213 (12.1%) zeros Zeros
song_hotttnesss has 1434 (14.3%) zeros Zeros
artist_hotttnesss has 496 (5.0%) zeros Zeros

Reproduction

Analysis started2025-06-24 10:24:23.837352
Analysis finished2025-06-24 10:24:30.772032
Duration6.93 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

track_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.6 KiB
2025-06-24T12:24:30.952734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters180000
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowTRBGGLA128F149C2EB
2nd rowTRBGGYE128F42B63AB
3rd rowTRBGGUL128F42ADE30
4th rowTRBGGRK128F422CA7F
5th rowTRBGGFE128E0785AC0
ValueCountFrequency (%)
trbggla128f149c2eb 1
 
< 0.1%
trbggbf128f425e4d1 1
 
< 0.1%
trbgjxw128f42ae7d8 1
 
< 0.1%
trbggul128f42ade30 1
 
< 0.1%
trbggrk128f422ca7f 1
 
< 0.1%
trbggfe128e0785ac0 1
 
< 0.1%
trbggru12903caaa2d 1
 
< 0.1%
trbggte128f424ecbc 1
 
< 0.1%
trbggwq128f42a88cf 1
 
< 0.1%
trbggot128f932dc65 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-06-24T12:24:31.168119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 17708
 
9.8%
1 13838
 
7.7%
F 12545
 
7.0%
A 12525
 
7.0%
R 11458
 
6.4%
T 11374
 
6.3%
8 11268
 
6.3%
9 8335
 
4.6%
3 7530
 
4.2%
4 7409
 
4.1%
Other values (26) 66010
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 17708
 
9.8%
1 13838
 
7.7%
F 12545
 
7.0%
A 12525
 
7.0%
R 11458
 
6.4%
T 11374
 
6.3%
8 11268
 
6.3%
9 8335
 
4.6%
3 7530
 
4.2%
4 7409
 
4.1%
Other values (26) 66010
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 17708
 
9.8%
1 13838
 
7.7%
F 12545
 
7.0%
A 12525
 
7.0%
R 11458
 
6.4%
T 11374
 
6.3%
8 11268
 
6.3%
9 8335
 
4.6%
3 7530
 
4.2%
4 7409
 
4.1%
Other values (26) 66010
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 17708
 
9.8%
1 13838
 
7.7%
F 12545
 
7.0%
A 12525
 
7.0%
R 11458
 
6.4%
T 11374
 
6.3%
8 11268
 
6.3%
9 8335
 
4.6%
3 7530
 
4.2%
4 7409
 
4.1%
Other values (26) 66010
36.7%

song_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.6 KiB
2025-06-24T12:24:31.316970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters180000
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowSOCRVCC12A6D4FB91B
2nd rowSOAPTWU12A8C1409CB
3rd rowSOELHNO12A8C13F09F
4th rowSOVBLUQ12A81C20920
5th rowSOGDAUG12A6701F302
ValueCountFrequency (%)
socrvcc12a6d4fb91b 1
 
< 0.1%
sobehxg12a8c138d22 1
 
< 0.1%
soqznyw12a8c1411b3 1
 
< 0.1%
soelhno12a8c13f09f 1
 
< 0.1%
sovbluq12a81c20920 1
 
< 0.1%
sogdaug12a6701f302 1
 
< 0.1%
sobbyxe12a58a772b2 1
 
< 0.1%
souuqxl12a8c1339fe 1
 
< 0.1%
sootbsz12a8c140223 1
 
< 0.1%
solbvsr12ab0183e1a 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-06-24T12:24:31.532402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18948
 
10.5%
A 16004
 
8.9%
2 13004
 
7.2%
O 11893
 
6.6%
S 11815
 
6.6%
8 9829
 
5.5%
C 8079
 
4.5%
B 8025
 
4.5%
0 6020
 
3.3%
F 6012
 
3.3%
Other values (26) 70371
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18948
 
10.5%
A 16004
 
8.9%
2 13004
 
7.2%
O 11893
 
6.6%
S 11815
 
6.6%
8 9829
 
5.5%
C 8079
 
4.5%
B 8025
 
4.5%
0 6020
 
3.3%
F 6012
 
3.3%
Other values (26) 70371
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18948
 
10.5%
A 16004
 
8.9%
2 13004
 
7.2%
O 11893
 
6.6%
S 11815
 
6.6%
8 9829
 
5.5%
C 8079
 
4.5%
B 8025
 
4.5%
0 6020
 
3.3%
F 6012
 
3.3%
Other values (26) 70371
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18948
 
10.5%
A 16004
 
8.9%
2 13004
 
7.2%
O 11893
 
6.6%
S 11815
 
6.6%
8 9829
 
5.5%
C 8079
 
4.5%
B 8025
 
4.5%
0 6020
 
3.3%
F 6012
 
3.3%
Other values (26) 70371
39.1%

title
Text

Distinct9708
Distinct (%)97.1%
Missing1
Missing (%)< 0.1%
Memory size763.5 KiB
2025-06-24T12:24:31.701947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length174
Median length98
Mean length19.446745
Min length1

Characters and Unicode

Total characters194448
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9483 ?
Unique (%)94.8%

Sample

1st rowThe Urban Gospel
2nd rowSee What Tomorrow Brings
3rd rowEla Edo Kardia Mou
4th rowLaakista humppa
5th rowCoral Fang (Album Version)
ValueCountFrequency (%)
the 1296
 
3.6%
version 773
 
2.2%
you 494
 
1.4%
album 466
 
1.3%
of 448
 
1.3%
a 437
 
1.2%
414
 
1.2%
in 409
 
1.2%
i 392
 
1.1%
me 349
 
1.0%
Other values (9939) 30036
84.6%
2025-06-24T12:24:32.253660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25590
 
13.2%
e 17668
 
9.1%
o 11705
 
6.0%
a 11139
 
5.7%
i 10338
 
5.3%
n 10276
 
5.3%
r 9120
 
4.7%
t 7822
 
4.0%
s 6980
 
3.6%
l 6720
 
3.5%
Other values (124) 77090
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25590
 
13.2%
e 17668
 
9.1%
o 11705
 
6.0%
a 11139
 
5.7%
i 10338
 
5.3%
n 10276
 
5.3%
r 9120
 
4.7%
t 7822
 
4.0%
s 6980
 
3.6%
l 6720
 
3.5%
Other values (124) 77090
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25590
 
13.2%
e 17668
 
9.1%
o 11705
 
6.0%
a 11139
 
5.7%
i 10338
 
5.3%
n 10276
 
5.3%
r 9120
 
4.7%
t 7822
 
4.0%
s 6980
 
3.6%
l 6720
 
3.5%
Other values (124) 77090
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25590
 
13.2%
e 17668
 
9.1%
o 11705
 
6.0%
a 11139
 
5.7%
i 10338
 
5.3%
n 10276
 
5.3%
r 9120
 
4.7%
t 7822
 
4.0%
s 6980
 
3.6%
l 6720
 
3.5%
Other values (124) 77090
39.6%
Distinct4412
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
2025-06-24T12:24:32.520400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length255
Median length137
Mean length13.3689
Min length1

Characters and Unicode

Total characters133689
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2163 ?
Unique (%)21.6%

Sample

1st rowShade Sheist / N.U.N.E. / King Arthur
2nd rowArc Angels
3rd rowIrini Merkouri
4th rowElakelaiset
5th rowThe Distillers
ValueCountFrequency (%)
the 885
 
4.0%
455
 
2.1%
john 127
 
0.6%
and 123
 
0.6%
of 116
 
0.5%
band 93
 
0.4%
featuring 87
 
0.4%
orchestra 84
 
0.4%
los 80
 
0.4%
dj 78
 
0.4%
Other values (5885) 19993
90.4%
2025-06-24T12:24:32.891503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12123
 
9.1%
e 12072
 
9.0%
a 10593
 
7.9%
i 8152
 
6.1%
n 7882
 
5.9%
r 7794
 
5.8%
o 7697
 
5.8%
s 5832
 
4.4%
l 5807
 
4.3%
t 4855
 
3.6%
Other values (93) 50882
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12123
 
9.1%
e 12072
 
9.0%
a 10593
 
7.9%
i 8152
 
6.1%
n 7882
 
5.9%
r 7794
 
5.8%
o 7697
 
5.8%
s 5832
 
4.4%
l 5807
 
4.3%
t 4855
 
3.6%
Other values (93) 50882
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12123
 
9.1%
e 12072
 
9.0%
a 10593
 
7.9%
i 8152
 
6.1%
n 7882
 
5.9%
r 7794
 
5.8%
o 7697
 
5.8%
s 5832
 
4.4%
l 5807
 
4.3%
t 4855
 
3.6%
Other values (93) 50882
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12123
 
9.1%
e 12072
 
9.0%
a 10593
 
7.9%
i 8152
 
6.1%
n 7882
 
5.9%
r 7794
 
5.8%
o 7697
 
5.8%
s 5832
 
4.4%
l 5807
 
4.3%
t 4855
 
3.6%
Other values (93) 50882
38.1%

year
Real number (ℝ)

Zeros 

Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean934.7046
Minimum0
Maximum2010
Zeros5320
Zeros (%)53.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:32.983560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32000
95-th percentile2008
Maximum2010
Range2010
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation996.65066
Coefficient of variation (CV)1.0662734
Kurtosis-1.9836379
Mean934.7046
Median Absolute Deviation (MAD)0
Skewness0.12847432
Sum9347046
Variance993312.53
MonotonicityNot monotonic
2025-06-24T12:24:33.220786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5320
53.2%
2006 320
 
3.2%
2005 304
 
3.0%
2007 285
 
2.9%
2004 270
 
2.7%
2003 254
 
2.5%
2008 253
 
2.5%
2009 250
 
2.5%
2001 217
 
2.2%
2002 198
 
2.0%
Other values (59) 2329
23.3%
ValueCountFrequency (%)
0 5320
53.2%
1926 2
 
< 0.1%
1927 3
 
< 0.1%
1929 1
 
< 0.1%
1930 2
 
< 0.1%
1934 1
 
< 0.1%
1935 2
 
< 0.1%
1936 1
 
< 0.1%
1940 2
 
< 0.1%
1947 2
 
< 0.1%
ValueCountFrequency (%)
2010 64
 
0.6%
2009 250
2.5%
2008 253
2.5%
2007 285
2.9%
2006 320
3.2%
2005 304
3.0%
2004 270
2.7%
2003 254
2.5%
2002 198
2.0%
2001 217
2.2%

duration
Real number (ℝ)

Distinct6553
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.50752
Minimum1.04444
Maximum1819.7677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:33.334771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.04444
5-th percentile105.67922
Q1176.0322
median223.05914
Q3276.37506
95-th percentile421.63285
Maximum1819.7677
Range1818.7233
Interquartile range (IQR)100.34286

Descriptive statistics

Standard deviation114.13751
Coefficient of variation (CV)0.47854891
Kurtosis27.433082
Mean238.50752
Median Absolute Deviation (MAD)49.763265
Skewness3.3796728
Sum2385075.2
Variance13027.372
MonotonicityNot monotonic
2025-06-24T12:24:33.437756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
222.14485 8
 
0.1%
216.81587 8
 
0.1%
181.39383 7
 
0.1%
229.51138 7
 
0.1%
232.202 7
 
0.1%
187.24526 6
 
0.1%
267.59791 6
 
0.1%
249.93914 6
 
0.1%
230.37342 6
 
0.1%
151.09179 6
 
0.1%
Other values (6543) 9933
99.3%
ValueCountFrequency (%)
1.04444 1
< 0.1%
1.2273 1
< 0.1%
3.5522 1
< 0.1%
3.99628 1
< 0.1%
6.08608 1
< 0.1%
7.13098 1
< 0.1%
9.66485 1
< 0.1%
10.34404 1
< 0.1%
10.65751 1
< 0.1%
10.68363 1
< 0.1%
ValueCountFrequency (%)
1819.76771 1
< 0.1%
1815.2224 1
< 0.1%
1686.7522 1
< 0.1%
1610.00444 1
< 0.1%
1598.1971 1
< 0.1%
1519.28118 1
< 0.1%
1400.37179 1
< 0.1%
1394.75546 1
< 0.1%
1280.88771 1
< 0.1%
1280.522 1
< 0.1%

tempo
Real number (ℝ)

Distinct9336
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.91545
Minimum0
Maximum262.828
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:33.531567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.14925
Q196.96575
median120.161
Q3144.01325
95-th percentile186.5015
Maximum262.828
Range262.828
Interquartile range (IQR)47.0475

Descriptive statistics

Standard deviation35.184412
Coefficient of variation (CV)0.2862489
Kurtosis0.4711858
Mean122.91545
Median Absolute Deviation (MAD)23.526
Skewness0.4112324
Sum1229154.5
Variance1237.9428
MonotonicityNot monotonic
2025-06-24T12:24:33.618199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
0.2%
131.991 5
 
0.1%
120.006 4
 
< 0.1%
128.006 4
 
< 0.1%
85.006 4
 
< 0.1%
131.999 4
 
< 0.1%
96.002 4
 
< 0.1%
117.298 4
 
< 0.1%
99.995 4
 
< 0.1%
96.007 4
 
< 0.1%
Other values (9326) 9938
99.4%
ValueCountFrequency (%)
0 25
0.2%
16.258 1
 
< 0.1%
19.657 1
 
< 0.1%
21.438 1
 
< 0.1%
26.663 1
 
< 0.1%
27.163 1
 
< 0.1%
30.048 1
 
< 0.1%
30.553 1
 
< 0.1%
30.827 1
 
< 0.1%
32.239 1
 
< 0.1%
ValueCountFrequency (%)
262.828 1
< 0.1%
258.677 1
< 0.1%
253.357 1
< 0.1%
248.079 1
< 0.1%
246.593 1
< 0.1%
246.5 1
< 0.1%
244.366 1
< 0.1%
244.268 1
< 0.1%
243.994 1
< 0.1%
243.981 1
< 0.1%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2761
Minimum0
Maximum11
Zeros1213
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:33.701324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5540867
Coefficient of variation (CV)0.67362003
Kurtosis-1.2844363
Mean5.2761
Median Absolute Deviation (MAD)3
Skewness-0.0083689462
Sum52761
Variance12.631532
MonotonicityNot monotonic
2025-06-24T12:24:33.771140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 1339
13.4%
0 1213
12.1%
2 1129
11.3%
9 1040
10.4%
1 821
8.2%
4 810
8.1%
5 795
8.0%
11 738
7.4%
10 700
7.0%
6 577
5.8%
Other values (2) 838
8.4%
ValueCountFrequency (%)
0 1213
12.1%
1 821
8.2%
2 1129
11.3%
3 310
 
3.1%
4 810
8.1%
5 795
8.0%
6 577
5.8%
7 1339
13.4%
8 528
 
5.3%
9 1040
10.4%
ValueCountFrequency (%)
11 738
7.4%
10 700
7.0%
9 1040
10.4%
8 528
 
5.3%
7 1339
13.4%
6 577
5.8%
5 795
8.0%
4 810
8.1%
3 310
 
3.1%
2 1129
11.3%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
6911 
0
3089 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6911
69.1%
0 3089
30.9%

Length

2025-06-24T12:24:33.851576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:24:33.907100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6911
69.1%
0 3089
30.9%

Most occurring characters

ValueCountFrequency (%)
1 6911
69.1%
0 3089
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6911
69.1%
0 3089
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6911
69.1%
0 3089
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6911
69.1%
0 3089
30.9%

loudness
Real number (ℝ)

Distinct7435
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.485668
Minimum-51.643
Maximum0.566
Zeros0
Zeros (%)0.0%
Negative9999
Negative (%)> 99.9%
Memory size78.3 KiB
2025-06-24T12:24:33.986090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-51.643
5-th percentile-20.93115
Q1-13.16325
median-9.38
Q3-6.5325
95-th percentile-4.02185
Maximum0.566
Range52.209
Interquartile range (IQR)6.63075

Descriptive statistics

Standard deviation5.3997882
Coefficient of variation (CV)-0.51496843
Kurtosis2.8625648
Mean-10.485668
Median Absolute Deviation (MAD)3.204
Skewness-1.357837
Sum-104856.69
Variance29.157713
MonotonicityNot monotonic
2025-06-24T12:24:34.086977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.748 6
 
0.1%
-7.736 6
 
0.1%
-13.615 5
 
0.1%
-6.53 5
 
0.1%
-5.75 5
 
0.1%
-12.256 5
 
0.1%
-10.7 5
 
0.1%
-6.947 5
 
0.1%
-9.224 5
 
0.1%
-9.464 5
 
0.1%
Other values (7425) 9948
99.5%
ValueCountFrequency (%)
-51.643 1
< 0.1%
-47.664 1
< 0.1%
-42.472 1
< 0.1%
-41.806 1
< 0.1%
-41.691 1
< 0.1%
-39.325 1
< 0.1%
-38.525 1
< 0.1%
-38.148 1
< 0.1%
-37.668 1
< 0.1%
-37.456 1
< 0.1%
ValueCountFrequency (%)
0.566 1
< 0.1%
-1.026 1
< 0.1%
-1.031 1
< 0.1%
-1.217 1
< 0.1%
-1.479 1
< 0.1%
-1.493 1
< 0.1%
-1.545 1
< 0.1%
-1.622 1
< 0.1%
-1.651 1
< 0.1%
-1.672 1
< 0.1%

danceability
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size586.1 KiB
0.0
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10000
100.0%

Length

2025-06-24T12:24:34.168634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:24:34.219845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

energy
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size586.1 KiB
0.0
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10000
100.0%

Length

2025-06-24T12:24:34.265732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:24:34.320164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20000
66.7%
. 10000
33.3%

song_hotttnesss
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1919
Distinct (%)34.0%
Missing4352
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean0.3428217
Minimum0
Maximum1
Zeros1434
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:34.383303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.36037058
Q30.5375039
95-th percentile0.72138646
Maximum1
Range1
Interquartile range (IQR)0.5375039

Descriptive statistics

Standard deviation0.24721972
Coefficient of variation (CV)0.72113207
Kurtosis-1.0408618
Mean0.3428217
Median Absolute Deviation (MAD)0.18115191
Skewness-0.029362954
Sum1936.257
Variance0.06111759
MonotonicityNot monotonic
2025-06-24T12:24:34.493984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1434
 
14.3%
0.2658610492 140
 
1.4%
0.2150803185 123
 
1.2%
0.3347065491 91
 
0.9%
0.2120454055 76
 
0.8%
0.2707759989 68
 
0.7%
0.3759843015 65
 
0.7%
0.3041695034 52
 
0.5%
0.2669551863 50
 
0.5%
0.4051157217 45
 
0.4%
Other values (1909) 3504
35.0%
(Missing) 4352
43.5%
ValueCountFrequency (%)
0 1434
14.3%
0.1878949793 1
 
< 0.1%
0.1903996306 1
 
< 0.1%
0.19065616 1
 
< 0.1%
0.1912999416 1
 
< 0.1%
0.1922477612 1
 
< 0.1%
0.1922643816 1
 
< 0.1%
0.1926477122 1
 
< 0.1%
0.1927269163 1
 
< 0.1%
0.1928563461 1
 
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%
0.9977583963 1
< 0.1%
0.9843467385 1
< 0.1%
0.9798371965 1
< 0.1%
0.9723868908 1
< 0.1%
0.9459946798 1
< 0.1%
0.9322741632 1
< 0.1%
0.9313464883 1
< 0.1%
0.9286168419 1
< 0.1%
0.9283671134 1
< 0.1%

artist_hotttnesss
Real number (ℝ)

High correlation  Zeros 

Distinct3714
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3855522
Minimum0
Maximum1.0825026
Zeros496
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:34.594160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.049034381
Q10.32526565
median0.38074233
Q30.45385807
95-th percentile0.60118606
Maximum1.0825026
Range1.0825026
Interquartile range (IQR)0.12859243

Descriptive statistics

Standard deviation0.1436473
Coefficient of variation (CV)0.37257548
Kurtosis2.5157572
Mean0.3855522
Median Absolute Deviation (MAD)0.062897272
Skewness-0.15228458
Sum3855.522
Variance0.020634547
MonotonicityNot monotonic
2025-06-24T12:24:34.694582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 496
 
5.0%
0.04903438085 13
 
0.1%
0.6066301517 12
 
0.1%
0.3862896512 12
 
0.1%
0.6672945956 12
 
0.1%
0.3200869268 12
 
0.1%
0.3114376403 12
 
0.1%
0.4794076756 11
 
0.1%
0.5442575057 11
 
0.1%
0.3604457658 11
 
0.1%
Other values (3704) 9398
94.0%
ValueCountFrequency (%)
0 496
5.0%
0.01156180408 1
 
< 0.1%
0.03530794717 2
 
< 0.1%
0.04903438085 13
 
0.1%
0.05018763941 2
 
< 0.1%
0.06372498646 6
 
0.1%
0.0778420103 1
 
< 0.1%
0.08016693866 10
 
0.1%
0.08229222642 1
 
< 0.1%
0.08999872435 3
 
< 0.1%
ValueCountFrequency (%)
1.082502557 9
0.1%
1.021255587 1
 
< 0.1%
1.005941966 5
0.1%
0.9224124434 1
 
< 0.1%
0.9160532283 6
0.1%
0.9082026192 8
0.1%
0.8792367447 2
 
< 0.1%
0.8728389206 2
 
< 0.1%
0.8724472232 1
 
< 0.1%
0.8546378282 6
0.1%

artist_familiarity
Real number (ℝ)

High correlation 

Distinct4047
Distinct (%)40.5%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.56545571
Minimum0
Maximum1
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-06-24T12:24:34.786396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32184343
Q10.46761116
median0.56366611
Q30.66802025
95-th percentile0.83927539
Maximum1
Range1
Interquartile range (IQR)0.20040909

Descriptive statistics

Standard deviation0.16016126
Coefficient of variation (CV)0.2832428
Kurtosis0.64489319
Mean0.56545571
Median Absolute Deviation (MAD)0.099232076
Skewness-0.2577576
Sum5652.2952
Variance0.025651628
MonotonicityNot monotonic
2025-06-24T12:24:34.884588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.2%
0.3345427837 13
 
0.1%
0.4702122957 12
 
0.1%
0.6869889546 12
 
0.1%
0.6069757372 12
 
0.1%
0.323008991 12
 
0.1%
0.8725365987 12
 
0.1%
0.8183193591 12
 
0.1%
0.5856831498 11
 
0.1%
0.8999349522 11
 
0.1%
Other values (4037) 9869
98.7%
ValueCountFrequency (%)
0 20
0.2%
0.01555788068 3
 
< 0.1%
0.0185015424 2
 
< 0.1%
0.01855509301 1
 
< 0.1%
0.01891756723 2
 
< 0.1%
0.01963088808 1
 
< 0.1%
0.02108265603 1
 
< 0.1%
0.02149290542 7
 
0.1%
0.02166644617 1
 
< 0.1%
0.02255650052 1
 
< 0.1%
ValueCountFrequency (%)
1 4
< 0.1%
0.9899385697 1
 
< 0.1%
0.951487818 1
 
< 0.1%
0.9473275066 9
0.1%
0.9418964545 2
 
< 0.1%
0.9392271646 1
 
< 0.1%
0.9379647504 5
0.1%
0.9349352674 7
0.1%
0.9339161064 5
0.1%
0.9290302874 7
0.1%

Interactions

2025-06-24T12:24:29.654211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:24.683921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.384107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.132977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.752572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.420337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.133090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.851913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.734215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:24.786687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.468693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.217803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.835161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.511235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.255144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.950883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.817477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:24.880999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.553519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.300516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.920338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.614486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.348873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.037318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.900403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:24.954049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.634610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.371135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.003412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.699997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.433094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.138719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.971387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.051545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.816055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.435091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.082824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.785721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.533143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.338970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:30.052788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.120451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.886383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.517664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.151785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.869537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.622580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.418483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:30.134085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.206983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.970354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.587837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.250512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.955148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.704006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.501426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:30.221056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:25.302581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.062597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:26.667611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:27.337090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.054061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:28.783580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T12:24:29.568350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-24T12:24:35.103037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
artist_familiarityartist_hotttnesssdurationkeyloudnessmodesong_hotttnessstempoyear
artist_familiarity1.0000.8600.0830.0380.2780.0210.5660.0630.398
artist_hotttnesss0.8601.0000.0760.0310.2330.0230.5560.0540.387
duration0.0830.0761.0000.0270.1380.0660.0440.0130.053
key0.0380.0310.0271.0000.0570.2890.0270.0090.002
loudness0.2780.2330.1380.0571.0000.0620.2590.1720.223
mode0.0210.0230.0660.2890.0621.0000.0170.0000.008
song_hotttnesss0.5660.5560.0440.0270.2590.0171.0000.0830.435
tempo0.0630.0540.0130.0090.1720.0000.0831.0000.055
year0.3980.3870.0530.0020.2230.0080.4350.0551.000

Missing values

2025-06-24T12:24:30.367419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-24T12:24:30.511561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-24T12:24:30.703324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_idsong_idtitleartist_nameyeardurationtempokeymodeloudnessdanceabilityenergysong_hotttnesssartist_hotttnesssartist_familiarity
0TRBGGLA128F149C2EBSOCRVCC12A6D4FB91BThe Urban GospelShade Sheist / N.U.N.E. / King Arthur0262.3473089.912110-7.6730.00.00.0000000.3461360.553284
1TRBGGYE128F42B63ABSOAPTWU12A8C1409CBSee What Tomorrow BringsArc Angels1992386.79465125.78781-10.1180.00.00.4733470.3493660.588932
2TRBGGUL128F42ADE30SOELHNO12A8C13F09FEla Edo Kardia MouIrini Merkouri0228.33587155.97930-6.2520.00.0NaN0.0000000.396304
3TRBGGRK128F422CA7FSOVBLUQ12A81C20920Laakista humppaElakelaiset199567.31710116.33890-2.4210.00.0NaN0.4051430.508440
4TRBGGFE128E0785AC0SOGDAUG12A6701F302Coral Fang (Album Version)The Distillers2003129.5930295.07590-3.6980.00.00.3603710.4885360.681091
5TRBGGRU12903CAAA2DSOBBYXE12A58A772B2He Don't Care (He's Stoned)Dan Hicks0159.9473072.35701-32.5350.00.00.0000000.3833570.594138
6TRBGGTE128F424ECBCSOUUQXL12A8C1339FEOne More Time (Kennedy Center Homecoming Version)The Katinas0159.2681280.94500-10.5990.00.00.0000000.4389330.606901
7TRBGGWQ128F42A88CFSOOTBSZ12A8C140223Rich GirlDaryl Hall & John Oates1976216.34567178.825101-5.7450.00.0NaN0.4963810.664924
8TRBGGBF128F425E4D1SOBEHXG12A8C138D22Rumour (Abstract Hip Hop Mix)bel canto199678.49751106.93671-17.7950.00.00.3458020.3682520.572338
9TRBGGOT128F932DC65SOLBVSR12AB0183E1A18 De NoviembreMaracaibo 150171.98975181.00221-8.0020.00.00.2998770.3147000.433954
track_idsong_idtitleartist_nameyeardurationtempokeymodeloudnessdanceabilityenergysong_hotttnesssartist_hotttnesssartist_familiarity
9990TRAFFEM128F42A2223SOQCUBJ12A8C13FEC6The Sirius Deception (Album Version)Nicholas Hooper0154.3832298.50911-19.7510.00.00.0000000.4358080.572943
9991TRAFFPR128F421A35FSOPRNQZ12A58A7B8E5Spaz's House Destruction PartyAnti-Flag2001184.05832131.12111-6.5470.00.00.5943620.5243790.844040
9992TRAFFSE128F429FB8DSOCIFJJ12A8C13F98CThis Heart Of MineJosh White0178.75546163.08340-21.8580.00.00.0000000.3510230.490586
9993TRAFFDW12903CE53B6SOJGCBK12AB01884D3Say What!?!Chris Standring2003269.32200140.04401-9.0410.00.00.5114240.4159380.564710
9994TRAFFEE12903CC04B4SORRHKT12AB0186469Blott En DagMons Leidvin Takle2001180.3750660.50971-16.0670.00.0NaN0.2824020.221726
9995TRAFFEQ12903CF4356SOHFNIA12AC4689741Catharsis Of A HereticThe Ocean2010128.83546139.97250-8.9660.00.00.6264640.4655000.636419
9996TRAFFCC128F9348D0DSOCMBTX12AB0181E7EUna Sopita De Tu Propio ChocolateAngélica María1997171.23220114.04501-11.1010.00.0NaN0.3099020.437890
9997TRAFFRG128F421BD23SOPMFLZ12A6D4F9D3AMai [Live]Josh Groban2008282.85342125.95891-7.3410.00.00.5205270.7551500.765357
9998TRAFFIW128F4236391SOZUQAZ12A81C21E0DVerdad AmargaLos Solitarios0168.1236394.99450-14.0580.00.00.2357140.2991130.488161
9999TRAFFKV12903CE29B5SOJFIUP12AB0187346Your LoveGlen Washington0234.68363102.60550-14.2130.00.0NaN0.3012470.512740